16 search results - page 1 / 4 » Active Learning in Multi-armed Bandits |
122
Voted
CORR
2010
Springer
14 years 9 months ago
2010
Springer
In the classic Bayesian restless multi-armed bandit (RMAB) problem, there are N arms, with rewards on all arms evolving at each time as Markov chains with known parameters. A play...
113
Voted
CORR
2008
Springer
15 years 1 months ago
2008
Springer
In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of n trials so as to maximize the total payoff of the chosen strategies. While ...
120
Voted
SAC
2005
ACM
15 years 6 months ago
2005
ACM
Active learning is a generic approach to accelerate training of classifiers in order to achieve a higher accuracy with a small number of training examples. In the past, simple ac...
126
Voted
CORR
2010
Springer
15 years 1 months ago
2010
Springer
We consider the restless multi-armed bandit (RMAB) problem with unknown dynamics. In this problem, at each time, a player chooses K out of N (N > K) arms to play. The state of ...
